Methods and systems for optimizing exercise compliance diagnostic parameters

ABSTRACT

A medical device detects certain patient activity based on a programmable activity threshold and determines the duration of detected activity. The activity threshold may be optimized by obtaining first and second duration measurements for at least one of a first activity session and second activity session. The first duration measurement is based on the activity threshold, while the second duration measurement is based on actual start and stop of the activity session. An adjustment of the activity threshold is suggested based on a correspondence between the first duration measurement and the second duration measurement of the first activity session, or a correspondence between the first duration measurement and the second duration measurement of the second activity session. One of the first and second activities is non-significant activity expected to be undetected by the device, while the other of the two activities is low-level activity expected to be detected by the device.

PRIORITY CLAIM

This application is a Divisional of U.S. patent application Ser. No.11/458,614 (Attorney Docket No. A04P3006-US3), filed Jul. 19, 2006,titled “Methods And Systems for Optimizing Exercise ComplianceDiagnostic Parameters.”

FIELD OF THE INVENTION

The present invention relates generally to exercise compliancediagnostics and more particularly to systems and methods for optimizingparameters used in such diagnostics.

BACKGROUND

An implantable cardiac device is,a medical device that is implanted in apatient to monitor electrical activity of the heart and to deliverappropriate electrical and/or drug therapy, as required. Implantablecardiac devices include, for example, pacemakers, cardioverters anddefibrillators. The term “implantable cardioverter defibrillator” orsimply “ICD” is used herein to refer to any implantable cardiac device.An ICD employs a battery to power its internal circuitry and to generateelectrical therapy. The electrical therapy can include, for example,pacing pulses, cardioverting pulses and/or defibrillator shock pulses.

Heart failure is a growing medical challenge. In clinical practicetoday, most patients are managed effectively through pharmacologicaltherapy such as beta-blockers, ACE inhibitors, and diuretics. If apatient's condition worsens, treatment may become more aggressive toinclude biventricular pacing and other implantable cardiac devicetherapy. Along with providing the primary objectives in the treatment ofheart failure of improving symptoms, increasing the quality of life, andslowing disease progression, devices need to provide heart failurephysicians with diagnostic parameters to monitor the patient's progress.

Currently, medical history and physical examination are the mostimportant tools that a physician uses to determine and mark the progressof a heart failure patient. This involves much of the physician's timewith the patient, as this may lead to the primary management program forthe patient.

Included in most management programs is an exercise routine. It has beenwritten extensively that adherence to exercise is a priority inimproving or in maintaining good heath. Exercise diagnostics may helpclinicians assess the compliance of the management programs prescribedto their patients, and possibly assist the patient in meeting thosegoals.

During exercise, the heart rate is a parameter or indicator of theamount of work that was required to provide blood and oxygen to thebody. The maximum heart rate for a level of exercise corresponds to theconditioning of the heart. Other parameters, such as heart rateintensity, percent oxygen consumption (%VO₂) reserve, metabolicequivalents (METS), and workload also provide data that is indicative ofheart conditioning.

Heart rate recovery after exercise is evaluated as a clinical marker ofgood vagal activity and cardiac health. As the heart rate increases dueto a reduction in vagal tone, the heart rate also decreases with areactivation of vagal activity. A delayed response to the decreasingheart rate may be a good prognostic marker of overall mortality (Cole,C. et al., NEJM 341:18, 1351-1357 (1999)) and cardiac health. Colesuggests that a reduction of only 12 beats per minute after one minutefrom peak exercise has been shown to be an abnormal value.

As previously mentioned, adherence to an exercise routine is a priorityin managing heart failure progression; therefore, it is critical that aphysician monitor significant patient activity, i.e., the time thepatient is moving around in a potential exercise-like manner, andpatient exercise, i.e., the time the patient is continuously movingaround in an exercise-like manner. One method of monitoring patientactivity and exercise relies on subjective and often inaccuratereporting of exercise duration and workload/intensity level by thepatient.

Other more objective methods of monitoring patient activity and exerciserely on algorithms that monitor for significant patient activity bycomparing patient activity data obtained through, physiological sensors,against an activity threshold. Such algorithms may employ an automatedprocess for initially setting the activity threshold using patientactivity data collected over a period of time, after implant of thedevice. For some people, however, the initial activity threshold settingmay overtime, result in system performance that is less than optimal.For example, an over-sensitive threshold value may cause the algorithmto consider a patient's daily activity such as office work, reading andtalking as significant activity. Conversely, an under-sensitivethreshold value may cause the algorithm to exclude low-level,significant activity, e.g., light walking, from its exercise diagnosticroutine. Therefore, periodic verification, recalibration or optimizationof the activity threshold is desirable to ensure accurate detection of,and distinction between, low-level, significant activity andnon-significant activity.

SUMMARY

Briefly, and in general terms the invention is directed topatient-associated medical devices that are operative to detect certainpatient activity based on a programmable activity threshold and todetermine the duration of detected activity. The activity threshold maybe optimized by obtaining first and second duration measurements for atleast one of a first activity session and second activity session. Thefirst duration measurement is based on the activity threshold, while thesecond duration measurement is based on actual start and stop of theactivity session. An adjustment of the activity threshold is suggestedbased on at least one of a correspondence between the first durationmeasurement and the second duration measurement of the first activitysession, and a correspondence between the first duration measurement andthe second duration measurement of the second activity session. One ofthe first and second activities is non-significant activity expected tobe undetected by the device, while the other of the two activities islow-level activity expected to be detected by the device.

These and other aspects and advantages of the invention will becomeapparent from the following detailed description and the accompanyingdrawings which illustrate by way of example the features of theinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a simplified diagram illustrating an exemplary ICD inelectrical communication with at least three leads implanted into apatient's heart for delivering multi-chamber stimulation and shocktherapy;

FIG. 1B is a functional block diagram of an exemplary ICD, which canprovide cardioversion, defibrillation and pacing stimulation in fourchambers of a heart;

FIG. 2 is a flow chart illustrating an embodiment of a method fordetermining an observed maximum heart rate of a patient during exercise;

FIGS. 3A-3D illustrate representations of sample exercise diagnosticresults;

FIG. 4 is a flow chart illustrating an embodiment of a method fordetermining an exercise diagnostic such as work of a patient duringexercise;

FIG. 5 is a flow chart illustrating an embodiment of a method fordetermining heart rate recovery of a patient after exercise;

FIG. 6 is a flow chart illustrating another embodiment of a method fordetermining an observed maximum heart rate of a patient during exercise,as described in a first example;

FIG. 7 is a flow chart illustrating another embodiment of a method fordetermining heart rate recovery of a patient, as described in a secondexample;

FIG. 8 is a flow chart illustrating an embodiment of a method fordetermining patient activity duration and exercise duration;

FIG. 9 is a flow chart illustrating an embodiment of a method fordetermining patient-specific offset parameters used in the method ofFIG. 8;

FIG. 10 is a flow chart illustrating an embodiment of a method fordetermining an activity threshold used in the method of FIG. 8;

FIG. 11 is an example of a histogram of activity correlation values thatmay be used to determine the activity threshold used in the method ofFIG. 8;

FIG. 12 is an exemplary time line illustrating the start and end ofactivity and exercise time periods;

FIG. 13A is a graph of heart rate reserve data and activity sensor dataas a function of time corresponding to patient activity on aStairmaster;

FIG. 13B is a graph of correlation values as a function of timecorresponding to the same patient activity, on a Stairmaster shown inFIG. 12 a;

FIG. 13C is a graph of heart rate reserve data and activity sensor dataas a function of time corresponding to patient activity on a treadmill;

FIG. 13D is a graph of correlation values as a function of timecorresponding to the same patient activity on a treadmill shown in FIG.12 c;

FIG. 14 is a functional block diagram of a system including a predictionmodel for optimizing an existing activity threshold;

FIG. 15 is a flow chart illustrating an embodiment of a method foroptimizing an activity threshold;

FIG. 16A is a graph of heart rate reserve and activity sensormeasurements as a function of time for a period of expected,non-significant patient activity; and

FIG. 16B is a graph of correlation values derived from the heart ratereserve and activity sensor measurements of FIG. 16A, as a function oftime and including examples of different activity thresholds.

DETAILED DESCRIPTION

The following description is of the best mode presently contemplated forpracticing the invention. This description is not to be taken in alimiting sense but is made merely for the purpose of describing thegeneral principles of the invention. The scope of the invention shouldbe ascertained with reference to the issued claims. In the descriptionof the invention that follows, like numerals or reference designationswill be used to refer to like parts or elements throughout.

It will be apparent to one of skill in the art that the presentinvention, as described below, may be implemented in many differentembodiments of hardware, software, firmware, and/or the entitiesillustrated in the figures. Any actual software and/or hardwaredescribed herein is not meant to limit the scope of the presentinvention. Thus, the structure, operation and behavior of the presentinvention will be described with the understanding that manymodifications and variations of the embodiments are possible, given thelevel of detail presented herein.

Before describing the invention in detail, it is helpful to describe anexample environment in which the invention may be, implemented. Thepresent invention is particularly useful in the environment of animplantable cardiac device. Implantable cardiac devices include, forexample, pacemakers, cardioverter-defibrillators, and hemodynamicmonitors. The term “implantable cardioverter defibrillator” or simply“ICD” is used herein to refer to any implantable cardiac device orimplantable cardioverter-defibrillator. FIGS. 1A and 1B illustrate suchan environment.

As shown in. FIG. 1A, there is an exemplary ICD 10 in electricalcommunication with a patient's heart 12 by way of three leads, 20, 24and 30, suitable for delivering multi-chamber stimulation and pacingtherapy. To sense atrial cardiac signals and to provide right atrialchamber stimulation therapy, ICD 10 is coupled to implantable rightatrial lead 20 having at least an atrial tip electrode 22, whichtypically is implanted in the patient's right atrial appendage.

To sense left atrial and ventricular cardiac signals and to provideleft-chamber pacing therapy, ICD 10 is coupled to “coronary sinus” lead24 designed for placement in the “coronary sinus region” via thecoronary sinus for positioning a distal electrode adjacent to the leftventricle and/or additional electrode(s) adjacent to the left atrium. Asused herein, the phrase “coronary sinus region” refers to thevasculature of the left ventricle, including any portion of the coronarysinus, great cardiac vein, left marginal vein, left posteriorventricular vein, middle cardiac vein, and/or small cardiac vein or anyother cardiac vein accessible by the coronary sinus.

Accordingly, exemplary coronary sinus lead 24 is designed to receiveatrial and ventricular cardiac signals and to deliver left ventricularpacing therapy using at least a left ventricular tip electrode 26, leftatrial pacing therapy using at least a left atrial ring electrode 27,and shocking therapy using at least a left atrial coil electrode 28.

ICD 10 is also shown in electrical communication with the patient'sheart 12 by way of an implantable right ventricular lead 30 having, inthis embodiment, a right ventricular tip electrode 32, a rightventricular ring electrode 34, a right ventricular (RV) coil electrode36; and an SVC coil electrode 38. Typically, right ventricular lead 30is transvenously inserted into heart 12 so as to place the rightventricular tip electrode 32 in the right ventricular apex so that RVcoil electrode 36 will be positioned in the right ventricle and SVC coilelectrode 38 will be positioned in the superior vena cava. Accordingly,right ventricular lead 30 is capable of receiving cardiac signals anddelivering stimulation in the form of pacing and shock therapy to theright ventricle.

FIG. 1B shows a simplified block diagram of ICD 10, which is capable oftreating both fast and slow arrhythmias with stimulation therapy,including cardioversion, defibrillation, and pacing stimulation. While aparticular multi-chamber device is shown, it is shown for illustrationpurposes only, and one of skill in the art could readily duplicate,eliminate or disable the appropriate circuitry in any desiredcombination to provide a device capable of treating the appropriatechamber(s) with the desired cardioversion, defibrillation and pacingstimulation.

A housing 40 of ICD 10, shown schematically in FIG. 1B, is oftenreferred to as the “can,” “case” or “case electrode” and may beprogrammably selected to act as the return electrode for all “unipolar”modes. Housing 40 may further be used as a return electrode alone or incombination with one or more of coil electrodes, 28, 36, and 38 forshocking purposes. Housing 40 further includes a connector (not shown)having a plurality of terminals, 42, 44, 46, 48, 52, 54, 56, and 58(shown schematically and, for convenience, the names of the electrodesto which they are connected are shown next to the terminals). As such,to achieve right atrial sensing and pacing, the connector includes atleast a right atrial tip terminal (AR TIP) 42 adapted for connection toatrial tip electrode 22.

To achieve left chamber sensing, pacing and shocking, the connectorincludes at least a left ventricular tip terminal (VL TIP) 44, a leftatrial ring terminal (AL RING) 46, and a left atrial shocking terminal(AL COIL) 48, which are adapted for connection to left ventricular ringelectrode 26, left atrial tip electrode 27, and left atrial coilelectrode 28, respectively.

To support right chamber sensing, pacing, and shocking the connectoralso includes a right ventricular tip terminal (VR TIP) 52, a rightventricular ring terminal (VR RING) 54, a right ventricular shockingterminal (RV COIL) 56, and an SVC shocking terminal (SVC COIL) 58, whichare configured for connection to right ventricular tip electrode 32,right ventricular ring electrode 34, RV coil electrode 36, and SVC coilelectrode 38, respectively.

At the core of ICD 10 is a programmable microcontroller 60 whichcontrols the various modes of stimulation therapy. As is well known inthe art, microcontroller 60 typically includes a microprocessor, orequivalent control circuitry, designed specifically for controlling thedelivery of stimulation therapy and can further include RAM or ROMmemory, logic and timing circuitry, state machine circuitry, and I/Ocircuitry. Typically, microcontroller 60 includes the ability to processor monitor input signals (data) as controlled by a program code storedin a designated block of memory. The details of the design ofmicrocontroller 60 are not critical to the present invention. Rather,any suitable microcontroller 60 can be used to carry out the functionsdescribed herein. The use of microprocessor-based control circuits forperforming timing and data analysis functions are well known in the art.In specific embodiments of the present invention, microcontroller 60performs some or all of the steps associated with the exercisediagnostics in accordance with the present invention.

Representative types of control circuitry that may be used with theinvention include the microprocessor-based control system of U.S. Pat.No. 4,940,052 (Mann et al.) and the state-machines of U.S. Pat. No.4,712,555 (Thornander et al.) and U.S. Pat. No. 4,944,298 (Sholder). Fora more detailed description of the various timing intervals used withinthe ICDs and their inter-relationship, see U.S. Pat. No. 4,788,980 (Mannet al.). The '052, '555, '298 and '980 patents are incorporated hereinby reference.

As shown in FIG. 1B, an atrial pulse generator 70 and a ventricularpulse generator 72 generate pacing stimulation pulses for delivery byright atrial lead 20, right ventricular lead 30, and/or coronary sinuslead 24 via an electrode configuration switch 74. It is understood thatin order to provide stimulation therapy in each of the four chambers ofthe heart, atrial and ventricular pulse generators 70, 72, may includededicated, independent pulse generators, multiplexed pulse generators,or shared pulse generators. Pulse generators 70 and 72 are controlled bymicrocontroller 60 via appropriate control signals 76 and 78,respectively, to trigger or inhibit the stimulation pulses.

Microcontroller 60 further includes timing control circuitry 79 which isused to control pacing parameters (e.g., the timing of stimulationpulses) as well as to keep track of the timing of refractory periods,PVARP intervals, noise detection windows, evoked response windows, alertintervals, marker channel timing, etc., which are well known in the art.Examples of pacing parameters include, but are not limited to,atrio-ventricular (AV) delay, interventricular (RV-LV) delay, atrialinterconduction (A-A) delay, ventricular interconduction (V-V) delay,and pacing rate.

Switch 74 includes a plurality of switches for connecting the desiredelectrodes to the appropriate I/O circuits, thereby providing completeelectrode programmability. Accordingly, switch 74, in response to acontrol signal 80 from microcontroller 60, determines the polarity ofthe stimulation pulses (e.g., unipolar, bipolar, combipolar, etc.) byselectively closing the appropriate combination of switches (not shown)as is known in the art.

Atrial sensing circuits 82 and ventricular sensing circuits 84 may alsobe selectively coupled to right atrial lead 20, coronary sinus lead 24,and right ventricular lead 30, through switch 74 for detecting thepresence of cardiac activity in each of the four chambers of the heart.Accordingly, the atrial (ATR. SENSE) and ventricular (VTR. SENSE)sensing circuits 82 and 84 may include dedicated sense amplifiers,multiplexed amplifiers, or shared amplifiers. Switch 74 determines the“sensing polarity” of the cardiac signal by selectively closing theappropriate switches, as is also known in the art. In this way, theclinician may program the sensing polarity independent of thestimulation polarity.

Each sensing circuit, 82 and 84, preferably employs one or more lowpower, precision amplifiers with programmable gain and/or automatic gaincontrol, bandpass filtering, and a threshold detection circuit, as knownin the art, to selectively sense the cardiac signal of interest. Theautomatic sensitivity control enables ICD 10 to deal effectively withthe difficult problem of sensing the low amplitude signalcharacteristics of atrial or ventricular fibrillation. Such sensingcircuits, 82 and 84, can be used to determine cardiac performance valuesused in the present invention.

The outputs of atrial and ventricular sensing circuits 82 and 84 areconnected to microcontroller 60 which, in turn, are able to trigger orinhibit atrial and ventricular pulse generators, 70 and 72,respectively, in a demand fashion in response to the absence or presenceof cardiac activity, in the appropriate chambers of the heart. Sensingcircuits 82 and 84, in turn, receive control signals over signal lines86 and 88 from microcontroller 60 for purposes of measuring cardiacperformance at appropriate times, and for controlling the gain,threshold, polarization charge removal circuitry (not shown), and timingof any blocking circuitry (not shown) coupled to the inputs of sensingcircuits 82 and 84.

For arrhythmia detection, ICD 10 utilizes the atrial and ventricularsensing circuits 82 and 84 to sense cardiac signals to determine whethera rhythm is physiologic or pathologic. The timing intervals betweensensed events (e.g., P-waves, R-waves, and depolarization signalsassociated with fibrillation which are sometimes referred to as“F-waves” or “Fib-waves”) are then classified by microcontroller 60 bycomparing them to a predefined rate zone limit (i.e., bradycardia,normal, low rate VT, high rate VT, and fibrillation rate zones) andvarious other characteristics (e.g., sudden onset, stability,physiologic sensors, and morphology, etc.) in order to determine thetype of remedial therapy that is needed (e.g., bradycardia pacing,anti-tachycardia pacing, cardioversion shocks or defibrillation shocks,collectively referred to as “tiered therapy”).

Microcontroller 60 utilizes arrhythmia detection circuitry 75 andmorphology detection circuitry 76 to recognize and classify arrhythmiaso that appropriate therapy can be delivered.

Cardiac signals are also applied to the inputs of an analog-to-digital(ND) data acquisition system 90. Data acquisition system 90 isconfigured to acquire intracardiac electrogram signals, convert the rawanalog data into a digital signal, and store the digital signals forlater processing and/or telemetric transmission to an external device102. Data acquisition system 90 is coupled to right atrial lead 20,coronary sinus lead 24, and right ventricular lead 30 through switch 74to sample cardiac signals across any pair of desired electrodes.

Advantageously, data acquisition system 90 can be coupled tomicrocontroller 60, or other detection circuitry, for detecting anevoked response from heart 12 in response to an applied stimulus,thereby aiding in the detection of “capture.” Capture occurs when anelectrical stimulus applied to the heart is of sufficient energy todepolarize the cardiac tissue, thereby causing the heart muscle tocontract. Microcontroller 60 detects a depolarization signal during awindow following a stimulation pulse, the presence of which indicatesthat capture has occurred. Microcontroller 60 enables capture detectionby triggering ventricular pulse generator 72 to generate a stimulationpulse, starting a capture detection window using timing controlcircuitry 79 within microcontroller 60, and enabling data acquisitionsystem 90 via control signal 92 to sample the cardiac signal that fallsin the capture detection window and, based on the amplitude, determinesif capture has occurred.

The implementation of capture detection circuitry and algorithms arewell known. See for example, U.S. Pat. No. 4,729,376 (DeCote, Jr.); U.S.Pat. No. 4,708,142 (DeCote, Jr.); U.S. Pat. No. 4,686,988 (Sholder);U.S. Pat. No. 4,969,467 (Callaghan et al.); and U.S. Pat. No. 5,350,410(Kleks et al.), which patents are hereby incorporated herein byreference. The type of capture detection system used is not critical tothe present invention.

Microcontroller 60 also contains maximum observed heart rate (HR_(max))detector 62, workload detector 64, and/or a heart rate recovery detector66. The operation of the HR_(max) detector, workload detector, and heartrate recovery detector are discussed below in connection with themethods of the present invention.

Microcontroller 60 is further coupled to a memory 94 by a suitabledata/address bus 96, wherein the programmable operating parameters usedby microcontroller 60 are stored and modified, as required, in order tocustomize the operation of ICD 10 to suit the needs of a particularpatient. Such operating parameters define, for example, pacing pulseamplitude, pulse duration, electrode polarity, rate, sensitivity,automatic features, arrhythmia detection criteria, and the amplitude,waveshape and vector of each shocking pulse to be delivered to thepatient's heart 12 within each respective tier of therapy.

Advantageously, the operating parameters of ICD 10 may be non-invasivelyprogrammed into memory 94 through a telemetry circuit 100 in telemetriccommunication with external device 102, such as a programmer,transtelephonic transceiver, or a diagnostic system analyzer. Telemetrycircuit 100 is activated by microcontroller 60 by a control signal 106.Telemetry circuit 100 advantageously allows intracardiac electrogramsand status information relating to the operation of ICD 10 (as containedin microcontroller 60 or memory 94) to be sent to external device 102through an established communication link 104.

For examples of such devices, see U.S. Pat. No. 4,809,697, entitled“Interactive Programming and Diagnostic System for use with ImplantablePacemaker” (Causey, Ill et al.); U.S. Pat. No. 4,944,299, entitled “HighSpeed Digital Telemetry System for Implantable Device” (Silvian); andU.S. Pat. No. 6,275,734, entitled “Efficient Generation of SensingSignals in an Implantable Medical Device such as a Pacemaker or ICD”(McClure et al.), which patents are hereby incorporated herein byreference.

In one embodiment, ICD 10 further includes a physiologic sensor 108 thatcan be used to detect changes in cardiac performance or changes in thephysiological condition of the heart. Accordingly, microcontroller 60can respond by adjusting the various pacing parameters (such as rate, AVDelay, RV-LV Delay, V-V Delay, etc.) in accordance with the embodimentsof the present invention. Microcontroller 60 controls adjustments ofpacing parameters by, for example, controlling the stimulation pulsesgenerated by the atrial and ventricular pulse generators 70 and 72.While shown as being included within ICD 10, it is to be understood thatphysiologic sensor 108 may also be external to ICD 10, yet still beimplanted within or carried by the patient. More specifically, sensor108 can be located inside ICD 10, on the surface of ICD 10, in a headerof ICD 10, or on a lead (which can be placed inside or outside thebloodstream). As discussed below, sensor 108 can also be used to measureactivity level.

ICD 10 additionally includes a battery 110 which provides operatingpower to all of the circuits shown in FIG. 1B. For ICD 10, which employsshocking therapy, battery 110 must be capable of operating at lowcurrent drains for long periods of time, and then be capable ofproviding high-current pulses (for capacitor charging) when the patientrequires a shock pulse. Battery 110 must also have a predictabledischarge characteristic so that elective replacement time can bedetected. Accordingly, ICD 10 preferably employs lithium/silver vanadiumoxide batteries, as is true for most (if not all) current devices.

ICD 10 further includes a magnet detection circuitry (not shown),coupled to microcontroller 60. It is the purpose of the magnet detectioncircuitry to detect when a magnet is placed over ICD 10, which magnetmay be used by a clinician to perform various test functions of ICD 10and/or to signal microcontroller 60 that the external programmer 102 isin place to receive or transmit data to microcontroller 60 throughtelemetry circuit 100.

As further shown in FIG. 1B, ICD 10 is shown as having an impedancemeasuring circuit 112 which is enabled by microcontroller 60 via acontrol signal 114. The known uses for an impedance measuring circuit120 include, but are not limited to, lead impedance surveillance duringthe acute and chronic phases for proper lead positioning ordislodgement; detecting operable electrodes and automatically switchingto an operable pair if dislodgement occurs; measuring respiration orminute ventilation; measuring thoracic impedance for determining shockthresholds; detecting when the device has been implanted; measuringstroke volume; and detecting the opening of heart valves, etc. Theimpedance measuring circuit 112 is advantageously coupled to switch 74so that any desired electrode may be used. The impedance measuringcircuit 112 is not critical to the present invention and is shown onlyfor completeness.

In the case where ICD 10 is intended to operate as a cardioverter, paceror defibrillator, it must detect the occurrence of an arrhythmia andautomatically apply an appropriate electrical therapy to the heart aimedat terminating the detected arrhythmia. To this end, microcontroller 60further controls a shocking circuit 116 by way of a control signal 118.The shocking circuit 116 generates shocking pulses of low (up to 0.5Joules), moderate (0.5-10 Joules), or high energy (11 to 40 Joules), ascontrolled by microcontroller 60. Such shocking pulses are applied tothe patient's heart 12 through at least two shocking electrodes (e.g.,selected from left atrial coil electrode 28, RV coil electrode 36, andSVC coil electrode 38). As noted above, housing 40 may act as an activeelectrode in combination with RV electrode 36, or as part of a splitelectrical vector using SVC coil electrode 38 or left atrial coilelectrode 28 (i.e., using the RV electrode as a common electrode).

Cardioversion shocks are generally considered to be of low to moderateenergy level (so as to minimize pain felt by the patient), and/orsynchronized with an R-wave and/or pertaining to the treatment oftachycardia. Defibrillation shocks are generally of moderate to highenergy level (i.e., corresponding to thresholds in the range of 5-40Joules), delivered asynchronously (since R-waves may be too disorganizedto be recognized), and pertaining exclusively to the treatment offibrillation. Accordingly, microcontroller 60 is capable of controllingthe synchronous or asynchronous delivery of the shocking pulses.

With the description of an example environment, such as an ICD, in mind,features of the present invention are described in more detail below.

A method 200 of determining a maximum observed heart rate (HR_(max)) ofa patient during exercise is illustrated in FIG. 2. According to anembodiment, the method 200 begins at step 202, in which the heart rateand activity level of the patient are monitored. The heart rate andactivity level of the patient may be continuously monitored during themethod 200.

The patient's heart rate may be determined by any suitable method. Manyvariations on how to determine heart rate are known to those of ordinaryskill in the art, and any of these of reasonable accuracy may be used.Heart rate can be determined by measurement of an R-R interval cyclelength (or P-P), which is the inverse of heart rate. As used herein, theheart rate (in beats per minute) can be seen as the inverse to cyclelength, determined by 60,000 divided by the cycle length (inmilliseconds).

Heart rate measurements can be produced based upon the monitored heartrate. Such heart rate measurements include but are not limited to heartrate and heart rate intensity.

The activity level of the patient may also be determined by any suitablemethod. For example, the activity level may be determined by anaccelerometer, piezoelectric crystal, minute ventilation,photoplethysmography, or a derivative thereof, such as the sensorindicated rate. In one embodiment, activity level is determined usingphysiologic sensor 108. In this embodiment, sensor 108 is anaccelerometer, a piezoelectric crystal, an impedance sensor, or aphotoplethysmography sensor.

In step 204, the measured activity level is compared with apredetermined activity threshold to determine whether the activity levelexceeds the threshold. The predetermined activity threshold can be avalue that corresponds to a certain level of exercise. It should beappreciated that the activity threshold value can be tailored for aspecific patient's condition. Illustratively, an activity thresholdvalue which correlates with walking or some other low level of exercisemay be, for example, 50 milligravities as measured by an accelerometer.

It should be understood that in the context of the present invention,when comparing a measurement to a threshold, the terms “exceeds” or “isgreater than” encompass instances when the measurement is equal to thethreshold value. Similarly, it should be understood that the terms“falls below” or “is less than” a threshold value encompass instanceswhen the measurement is equal to the threshold value. A person skilledin the relevant art will recognize that selection of a threshold value,and how to treat the condition of equality between the threshold and themeasurement are design choices.

The activity level can be compared with an activity threshold at varioustime intervals or periodically to determine whether the activity levelexceeds the predetermined threshold. The particular selected timeinterval for monitoring is not critical. In one embodiment of theinvention, the activity level is monitored and compared with theactivity threshold at time intervals of 30 seconds (i.e., every 30seconds).

If the patient activity level exceeds the predetermined activitythreshold, then the method proceeds to step 206. Illustratively, if 50milligravities activity is a threshold that correlates well with walkingor some low level of exercise and the implantable medical device isprogrammed at this threshold, then if the measured activity levelexceeds 50 milligravities, the method proceeds to step 206.

Steps 206 and 208 can be performed when the patient activity levelexceeds the predetermined activity threshold for a predetermined periodof time. This predetermined period of time can be an amount that oneskilled in the art would understand to be sufficient for the heart toreact to the exercise by the patient (which can be indicated by e.g.,the activity level exceeding the predetermined activity threshold).Illustratively, the predetermined period of time may be 10 seconds tofive minutes, preferably about two to three minutes, more preferablyabout two minutes.

In step 206, a heart rate measurement is compared with a stored heartrate measurement. The stored heart rate measurement can be, for example,a heart rate measurement previously obtained during exercise, includinga previously determined HR_(max) during exercise. Prior to firstoccurrence of the method, the stored heart rate measurement can be setto a predetermined default value. If the heart rate measurement exceedsthe previously stored heart rate measurement, then the method proceedsto step 208. Otherwise, step 204 is repeated. That is, the methodcontinues to monitor heart rate and activity level and produce heartrate measurements.

In step 208, the difference between the heart rate measurement and thestored heart rate measurement is compared to a predetermined threshold.The predetermined threshold difference may be selected to correspond toa value above which may be indicative of noise, PACs, PVCs, and/orarrhythmias. If the difference between the heart rate measurement andthe stored heart rate measurement exceeds the threshold difference, themeasured heart rate is not considered to be a HR_(max). The thresholdmay even be step-size units, so as to show a gradual (physiologic)increase.

In accordance with one embodiment, step 208 is not performed. However,this embodiment is less preferred, as the resulting HR_(max) could beinaccurate due to noise and/or premature heartbeats.

It should be understood that the order of comparison steps 206 and 208is not limited to that depicted in the figure and may be performed inreverse order or conducted simultaneously.

If, in step 206, the heart rate measurement is greater than the storedheart rate measurement and, in step 208, the difference between theheart rate measurement and the stored heart rate measurement does notexceed a predetermined threshold, then the heart rate associated withthe heart rate measurement may be identified as a maximum observed heartrate (HR_(max)). In other words, a heart rate can be identified as aHR_(max) when the comparison steps 204, 206, and 208 are met.

The maximum observed heart rate may be recorded as a stored value, andthe method 200 repeated, using the HR_(max) as a new stored heart ratemeasurement. The HR_(max) determination may be continued until activitylevel and/or heart rate is indicative of a slow-down of exercise.

Based on the HR_(max) obtained, further values may be obtained that areindicative of heart conditioning. These values include heart rateintensity, percent oxygen consumption (%VO₂) reserve, metabolicequivalents (METS), percentage METS, workload, and absolute oxygenuptake.

For example, heart rate intensity (also known as percent heart ratereserve, heart rate capacity, target heart rate, or % HRR) may becalculated by dividing HR_(max) by the predicted age compensated maximumheart rate as follows:

${\% \mspace{14mu} {HRR}} = {\frac{{HR}_{\max} - {{resting}\mspace{14mu} {HR}}}{{{Age}\mspace{14mu} {Compensated}\mspace{14mu} {Maximum}\mspace{14mu} {HR}} - {{resting}\mspace{14mu} {HR}}} \times 100}$

In the, equation indicated above, resting heart rate of the patient maybe obtained by any suitable method including, for example, a heart ratemeasurement taken when the activity level of the patient is sufficientlylow to be considered inactive. The age compensated maximum heart ratecan be calculated by the formula: (220-age).

With %HRR, it is also possible to calculate %VO₂ reserve. Swain et al.have shown a close correlation between %HRR and %VO₂ reserve (“Heartrate reserve is equivalent to %VO₂ reserve, not %VO_(2max) ,” Med Sci.Sports Exercise 29:410-414 (1997)).

%VO₂ reserve is an intensity scale or index that describes thepercentage of oxygen intake used during exercise. The value between %VO₂reserve and 100% is the amount of oxygen intake reserves available. Thisvalue may be obtained by the following equation (Swain et al., Target HRfor the development of CV fitness, Medicine & Science in Sports &Exercise 26(1):112-116):

%VO₂ reserve=(%HRR−37)/0.64

where %HRR is calculated as described above.

Workload is measure of intensity times duration, and may be seen by thefollowing equation:

Workload=Intensity*Duration

where Intensity is VO_(2observed), but may also be seen as an index suchas heart rate intensity (%HRR) or %VO₂ reserve as discussed above, andDuration is the time during exercise when activity is above apredetermined threshold. It is possible to use in the calculation ofwork only %HRR values above a predetermined threshold (e.g., >40%),reflective of at least moderate exercise. An additional method involvesmultiplying the mean %HRR above the predetermined threshold by the totalduration.

A primary expression of intensity throughout the clinical community ismetabolic equivalents (METS). METS is a measure of Intensity orfunctional capacity. One (1) MET is equivalent to the amount of energyused at rest (oxygen uptake of 3.5 ml/(kg*min)), or the resting VO₂.

1 MET=3.5 mL/(kg*min)=VO_(2 resting)

METS are linked to heart rate intensity. See, Strath et al., “Evaluationof Heart Rate as a Method for Accessing Moderate Intensity PhysicalActivity,” Med. & Sci. in Sports & Exerc., 465-470 (2000).

One method for determining METS has been described by Wilkoff, B. L., etal. (“A Mathematical Model of the Cardiac Chronotropic Response toExercise,” J. Electrophysiol. 3:176-180 (1989)), in which a mathematicalmodel was developed describing the relationship of percentage metabolicequivalents (%METS) to heart rate intensity using the CAEP and Bruceexercise protocols. They found that the relationship was linear, with aslope of approximately 1 (1.06), by the equation:

%METS=1.06*(%HRR)−4.87

Observed METS during exercise can be obtained through the, followingequation:

${\% \mspace{14mu} {METS}} = {\frac{\left( {{METS}_{observed} - {METS}_{rest}} \right)}{\left( {{METS}_{\max} - {METS}_{rest}} \right)}*100\%}$with  METS_(rest) = 1

The value for METS_(max) to be used in the above equation may beobtained as follows:

Predicted METS_(max)=16.6−0.16(age)

This predicted METS_(max) value is an approximation, as it was obtainedby a nomogram of sedentary men who participated in the USAir ForceSchool of Aerospace Medicine Protocol, and who did not have a history ofCHF. See Morris et al., “Nomogram Based on Metabolic Equivalents and Agefor Assessing Aerobic Exercise Capacity in Men,” J. Am. Coll. Cardiol.22:175-182 (1993). However, if this approximation is used as a best fitmethod for maximal METS expected for each patient, METS_(observed) canthus be calculated as:

METS_(observed)=(%METS/100)*((16.6−0.16*(age))−1)+1

METS can also be determined by the following method by alternativelysolving for %VO₂ reserve. %VO₂ reserve can be calculated by thefollowing equation:

${\% \mspace{14mu} {VO}_{2}\mspace{14mu} {reserve}} = {\frac{\left( {{VO}_{2{observed}} - {VO}_{2{rest}}} \right)}{\left( {{VO}_{2\max} - {VO}_{2{rest}}} \right)}*100\%}$with  VO_(2rest) = 1

where VO_(2max) can be obtained from the non-exercise predictionequation of Jackson et al., “Prediction of functional aerobic capacitywithout exercise testing,” Med. Sci. Sports & Exerc J. 22:863-870 (1990)by:

VO₂=50.513+1.589*(activity scale[0 . . .7])−0.289*(age)−0.552*(%fat)+5.863*(F=0, M=1).

Or, for those times when % fat may be difficult to obtain, the followingequation by Jackson et al. allows for use of Body Mass Index (BMI):

VO_(2max)=56.363+1.921*(activity scale[0 . . .7])−0.381*(age)−0.754*(BMI)+10.987*(F=0, M=1)

In the above two equations for VO_(2max), activity scale can be relatedto %HRR as a level of activity, % fat or BMI is either calculated as anaverage over the population or a value to be uploaded to the ICD, and Fand M designate female and male, respectively.

When VO_(2max) is plugged back into the %VO₂ equation, VO_(2observed)can be obtained (units of mL/[kg*min]). METS_(observed) can be obtainedby dividing VO_(2observed) by3.5.

Another way to determine VO_(2max) is by the Astrand single-stagesubmaximal method, with the following equation:

VO_(2max)=VO_(2observed)*[(Age compensated max. HR−K)/(HR_(observed)−K)]

where K=63 for men and 73 for women. (Astrand, P. O., and Rodah, K.,Textbook of Work Physiology, 3^(rd) Ed. New York: McGraw-Hill, 1986, p.318-325 and 340-358.)

Once METS_(observed) has been calculated, it is possible to get thefollowing values:

Relative Oxygen consumption (ml/(kg*min)): METS/3.5

Absolute Oxygen Uptake (L/min): VO_(2max)*Weight

Calories (kcal): 1L O₂=5 kcal: (VO_(2max)* duration)/5

Joules: 1 Kcal=4186 J

If the value for METS has a large standard deviation over the aboveequations, it can be further worked into a descriptive intensity scale(light, moderate, vigorous) as defined by Ainsworth B E et al.,Compendium of physical activities: an update of activity codes and METintensities, Med Sci. Sports Exerc.; 9:S498-S516 (2000)) where these canbe defined by:

METS_(60%max cardiorespiratory capacity)=[0.6*(60-0.55*(age)]/3.5 formen, and

METS_(60%max cardiorespiratory capacity)=[0.6*(48-0.37*(age)]/3.5 forwomen

with 60% max cardiorespiratory capacity (MCC) considered vigorous.Therefore, light intensity would be, for example, between 20-40%, andmoderate activity would be, for example, between 40-60%.

FIGS. 3A to 3D illustrate how exercise data, such as exerciseintensities may be displayed. The data illustrated in these Figures areprophetic. In FIG. 3A, measured heart rate intensity (%HRR) data isdisplayed as a function of time in the graph. The table above the chartillustrates the corresponding time to HR_(max) and the total duration ofHR_(max). In FIG. 3B, measured heart rate intensity and total durationof HR_(max) are illustrated on one graph. In FIG. 3C, measured exerciseintensity in the units of METS is illustrated, with correspondingamounts of vigorous, moderate, and low intensities, and the duration ofeach amount. In FIG. 3D, measured workload is illustrated, withcorresponding duration of the workload. Each data point illustrated onthe table and graphs of FIGS. 3A-3D represent an average over one week.

As is illustrated from FIGS. 3A-3D, the invention also encompassesdetermining the time period associated with exercise intensities. Forexample, the time to and duration of HR_(max) and workload can bedetermined.

The above-described method 200 for determining the maximum observedheart rate of a patient during exercise may be implemented by hardware,software, or firmware of a pacing system, such as the ICD describedearlier with reference to FIGS. 1A and 1B, with particular reference toHR_(max) detector 62.

In another embodiment, a method for determining exercise diagnostics,such as workload, heart rate intensity, percent oxygen consumption (%V0₂) reserve, metabolic equivalents (METS), percentage METS, and absoluteoxygen uptake may be obtained without obtaining HR_(max). This methodincludes monitoring a changing heart rate of a patient and producingheart measurements, monitoring activity level, and determining anexercise diagnostic, such as workload of the patient using at least oneheart rate measurement when the activity level exceeds an activitythreshold.

A method 400 of determining workload of a patient during exercise isillustrated in FIG. 4. According to an embodiment, the method 400 beginsat step 402, in which the heart rate and activity level of the patientis monitored. The heart rate and activity level of the patient may becontinuously monitored during the method 400.

As discussed above in conjunction with the method for determiningHR_(max), the patient's heart rate and activity level may be determinedby any suitable method, and heart rate measurements can be generatedbased upon the monitored heart rate. In embodiments, the heart ratemeasurements include heart rate intensity.

In step 404, the measured activity level is compared with apredetermined activity threshold to determine whether the activity levelexceeds the threshold. As discussed above, the predetermined activitythreshold can be a value that corresponds to a certain level of exerciseand can be tailored for a specific patient's condition.

The activity level can be compared with an activity threshold at varioustime intervals to determine whether the activity level exceeds thepredetermined threshold for a predetermined period of time. The timeinterval or frequency of comparing the activity level with the activitythreshold is not critical to the invention. In an embodiment, theactivity level is monitored and compared with the activity threshold attime intervals of 30 seconds.

If it is determined in step 404 that the patient activity level exceedsa predetermined activity threshold, then step 406 is performed. Asdiscussed above in conjunction with determining HR_(max), step 406 canbe performed when the patient activity level exceeds the predeterminedactivity threshold for at least a predetermined period of time. Thispredetermined period of time may correlate to the amount of time for theheart to react to the exercise by the patient. Illustratively, thepredetermined period of time may be 10 seconds to five minutes,preferably about two to three minutes, more preferably about twominutes.

In step 406, workload of the patient is determined using at least oneheart rate measurement. Preferably, a heart rate measurement that isused to determine work of the patient during the exercise is heart rateintensity.

Specifically, workload of a patient during exercise can be determined bythe summation of intensities over time over the full time of exercise(i.e., for the entire period that the activity level exceeds thepredetermined threshold), where intensities are calculated from, theprevious equations discussed to obtain VO_(2observed). Alternately, asdiscussed above, workload may be described as a unitless index bymultiplying intensities such as %HRR or %VO₂ reserve and timeIllustratively, after the activity level exceeds an activity threshold,work values can be calculated (Intensity * Duration) for each data-pointuntil the cessation of exercise (i.e., when the activity level no longerexceeds the predetermined threshold). The determination of work of thepatient during the exercise can also be represented by the followingformula:

ΣIntensity (x)*(Time(x)−Time(x−1))

where x=0:n.

Based on the workload value obtained above, other exercise diagnostics,such as heart rate intensity, percent oxygen consumption (%VO₂) reserve,metabolic equivalents (METS), percentage METS, and absolute oxygenuptake may be obtained. For example, heart, rate intensity may be foundby dividing the work by the total time of exercise.

The above-described method 400 for determining workload of a patientduring exercise may be implemented by hardware, software, or firmware ofa pacing system, such as the ICD described earlier with reference toFIGS. 1A and 1B, with particular reference to work detector. 64.

Heart rate recovery involves analyzing how the heart recovers from amaximum rate during exercise. The heart rate recovery value may notchange in a matter of days, but possibly in a matter of weeks. Obtainingthe heart rate recovery value only during episodes of peak exercise, asopposed to any low-level exercise, may provide a more accuratereflection of cardiac health through heart rate recovery.

A method 500 of determining a measure of heart rate recovery isillustrated in FIG. 5. According to an embodiment, the method 500 beginsat step 502, in which the heart rate and activity level of the, patientare monitored. The heart rate and activity level of the patient may becontinuously monitored during the method 500. The heart rate andactivity level can be monitored by any suitable method, including thosediscussed above.

Heart rate measurements can be produced based upon the monitored heartrate. As discussed above, such heart rate measurements include but arenot limited to heart rate and heart rate intensity.

In step 504, a heart rate measurement is compared with a first heartrate measurement threshold, and an activity level is compared with afirst activity threshold. The first heart rate measurement threshold andfirst activity threshold may be indicative of exercise, preferablyvigorous or peak exercise.

In step 504, if a heart rate measurement exceeds a first heart ratemeasurement threshold, and/or an activity level exceeds a first activitythreshold, then the method proceeds to step 506. In step 506, the heartrate is identified as a first heart rate. That is, the heart rate takenat the time (1) a heart rate measurement exceeded a first heart ratemeasurement threshold and/or (2) the activity level exceeded the firstactivity threshold is used as a first heart rate value for furthercomputations.

In one embodiment, in step 504, the first heart rate is identified whenat least one heart rate measurement exceeds the first heart ratemeasurement threshold. In another embodiment, the first heart rate isidentified when at least one heart rate measurement exceeds the firstheart rate measurement threshold for a predetermined period of time. Inother embodiments, the first heart rate can be identified when anaverage value of heart measurements (taken over a predetermined timeperiod, such as, for example, one minute) exceeds the first heart ratemeasurement threshold.

In another embodiment, the first heart rate can be identified when theactivity level exceeds the first activity threshold. In yet anotherembodiment, the first heart rate can be identified when the activitylevel exceeds the first activity threshold for a predetermined period oftime In still yet another embodiment, the first heart rate can beidentified when for the predetermined period of time an average activitylevel exceeds the first activity threshold.

Preferably, the first heart rate is identified when both the activitylevel exceeds a first activity threshold and a heart rate measurementexceeds a first heart rate measurement threshold.

Even more preferably, the first heart rate is identified when the meanactivity level value exceeds a first activity threshold for apredetermined period of time, and a mean heart rate measurement value,such as heart rate intensity, exceeds a first heart rate measurementthreshold for a predetermined period of time.

In accordance with some embodiments, the first heart rate is identifiedonly during peak exercise, only after a stringent set of conditions havebeen met. These conditions can include certain levels of heart rateintensity, activity level and duration of time. This first heart ratemay be referred to as a peak exercise heart rate.

Illustratively, a peak exercise heart rate can be identified when themean activity level exceeds a first activity threshold and the heartrate intensity exceeds a heart rate intensity threshold, such as, e.g.,80%, for a period of time of at least about five minutes.

As illustrated by step 508, heart rate and activity level continue to bemonitored. It should be understood that the identified first heart ratecan be overwritten by a subsequent heart rate (including a slower heartrate), provided that the first heart rate criteria described above arestill met.

Heart rate and activity level also continue to be monitored, aillustrated by step 508, for determining the next parameter used todetermine heart rate recovery, a second heart rate. The second heartrate is the heart rate after a slow-down in exercise, and is comparedwith the first heart rate to determine a measure of heart rate recovery.In accordance with some embodiments, heart rate measurements (such asfor example heart rate) continued to be produced.

In step 510, a heart rate measurement is compared with a second heartrate measurement threshold, and an activity level is compared with asecond activity threshold. The second heart rate measurement thresholdand second activity threshold can be indicative of a slowing down orcessation of exercise.

If a heart rate measurement falls below a second heart rate measurementthreshold, and/or an activity level falls below a second activitythreshold, then in step 512 the monitoredheart rate is identified as asecond heart rate.

In one embodiment, the second heart rate is identified when the activitylevel falls below the second activity threshold for a predeterminedperiod of time. Preferably, the second heart rate is identified when amean activity level falls below the second activity threshold for apredetermined period of time. The comparison can also be done based onan average activity level over a predetermined period of time.

In another embodiment, the second heart rate is identified when a heartrate measurement falls below a second heart rate measurement thresholdfor a predetermined period of time. For example, if a heart ratemeasurement (e.g. heart rate) falls below a predetermined thresholdand/or the mean activity level falls below a predetermined activitythreshold, then the heart rate and activity levels can be recorded for apredetermined period of time, such as, for example one, two, or threeminutes.

After the predetermined period of time, if a heart rate measurement isless than the heart rate measurement prior to the predetermined periodof time, and the activity level is less than a third activity threshold(which can be the same as or lower than the second activity threshold),then a second heart rate is identified. Preferably, the slowest heartrate measured during the predetermined period of time is identified asthe second heart rate.

In step 514, once a first heart rate and a second heart rate areidentified, the first and second heart rates are used to determine ameasure of heart rate recovery. For example, the second heart rate issubtracted from the first heart rate to obtain a heart rate difference.The difference is a heart rate recovery value.

It should be understood that additional second heart rate values can beidentified after the first heart rate and compared to the first heartrate to determine a measure of heart rate recovery. Accordingly, theterm “second heart rate” is intended to encompass one or more heartrates that meet the above-described criteria for identification of thesecond heart rate. In other words, the second heart rate may be severalheart rates over consecutive periods of time (e.g. minutes).

Illustratively, heart rates measured at discrete times after theidentified first heart rate and that meet the second heart rateidentification criteria can be compared with the first heart rate todetermine a measure of heart rate recovery. For example, the differencebetween the first heart rate and each of the second heart rates canprovide a measure of heart rate recovery. Also, a listing of the firstheart rate and heart rates meeting the second heart rate criteria asthey decrease over time can also be a measure of heart rate recovery.

The invention also encompasses identifying the first heart rate at thetime the criteria for identifying the second heart rate is met. Forexample, if a heart rate measurement exceeds a first heart ratemeasurement threshold or an activity level exceeds a first activitythreshold, and subsequently a heart rate measurement falls below asecond heart rate measurement or the activity level falls below a secondactivity threshold, a first heart rate can be identified at or near theinflection point between meeting the first and second heart rateidentification criteria.

The second heart rate then can be identified as one or more heart ratesmeasured subsequent to the identified first heart rate. For example,provided that the measured heart rates meet the second heart rateidentification criteria, a second heart rate can be identified oneminute, two minutes, and/or three minutes following the first heartrate. The difference between the first heart rate and the second heartrate at one, two, and/or three minutes post-first heart rateidentification provides values that determine a measure of heart raterecovery.

To illustrate, a patient exercises (e.g. runs) for five minutes, andthen stops running and sits down for three minutes. Provided that thepatient met the first and second heart rate identification criteriadescribed above, the first heart rate would be identified at the fiveminute mark, and the second heart rates would be identified at the six,seven, and eight minute mark. The first heart rate would be comparedwith each of the second heart rates at the six, seven, and eight minutemark to determine a measure of heart rate recovery.

Preferably, in the method 500 for determining a measure of heart raterecovery, heart rate measurements are filtered to remove noise andpremature heart beats such as arrhythmias, PACs, and PVCs.

The above-described method 500 for determining the measure of heart raterecovery of a patient may be implemented in software, or firmware of apacing system, such as the ICD described earlier with reference to FIGS.1A and 1B, with particular reference to HR Recovery Detector 66.

A method for determining a maximum observed heart rate (HR_(max)) of apatient during exercise is illustrated in FIG. 6.

To calculate HR_(max) for exercise conditioning, it is preferred thatthe patient has maintained a certain level of activity for a certainperiod of time. Thus, in the illustrative method, the maximum observedheart rate is not calculated unless the activity level is above athreshold activity for a certain period of time.

In method 600, the current cycle length (inverse of heart rate) andactivity level are obtained as illustrated in step 602. It should beunderstood that the cycle length and activity level can be continuouslyor periodically monitored.

In step 604, the activity level measured is compared with an activitythreshold. If the activity is less than an activity threshold, then themethod returns to step 602. In this manner, steps 602 and 604 result ina continuous (or, optionally, periodic) monitoring of activity level.

If the measured activity level is greater than the activity threshold,then in step 606 the elapsed time (i.e., the period during which theactivity level is greater than the activity threshold) is compared witha time threshold. The time threshold can be, for example, 2-3 minutes.Once this comparison indicates that sufficient time has elapsed, thenstep 608 is performed. Thus, before step 608 is performed, there hasbeen a sufficient activity level for a sufficient period of time toindicate actual exercise by the patient.

In step 608, the current cycle length is compared with the previouscycle lengths, preferably a previous average cycle length. In step 610,if the difference of cycle length is too large (e.g., greater than orequal to about 100 milliseconds), this may indicate noise, PACs, PVCs orarrhythmias, and will not be identified as the HR_(max). If this occurs,as illustrated in step 610, the method returns to step 602 to obtain anew, current cycle length and activity.

In step 610, if the difference of cycle length is less than a threshold(indicating that the current cycle length is not due to noise or apremature heart beat), then in step 612, the current cycle length iscompared with the previous cycle length. If the current recorded cyclelength is not less than the previously recorded value, then the currentcycle length is not identified as the HR_(max), and step 602 isrepeated. However, if the current cycle length is less than the previousrecorded value, then in step 614 the current cycle length is identifiedand stored as the new HR_(max).

A method for determining heart rate recovery of a patient is illustratedin FIG. 7.

In accordance with the illustrated method 700, the first heart rate, apeak exercise heart rate is obtained by analyzing cycle lengths onlywhen the qualifications for HR_(max) have been met.

In step 702, the current cycle length is obtained. In step 704, if thecycle length is near HR_(max), the value of the heart rate intensity isdetermined. If this value is greater than a predetermined threshold suchas, e.g., 65%, and if the length of time is greater than a predeterminedthreshold, such as, e.g., 5 minutes, then in step 706 the cycle lengthis recorded as the first, peak exercise heart rate. Otherwise, the heartrate intensity for each cycle length continues to be determined.

The cycle length recorded in step 706 is continuously or periodicallyrecorded, and may be overwritten by slower rates. However, if anoticeable slowdown occurs, in step 708 a new buffer collects therecorded cycle length. In step 710, if the current cycle length isgreater than a predetermined threshold, such as, e.g., 20 milliseconds,or the mean activity level is less than a predetermined threshold, eachindicative of a drop in activity, then in step 712 cycle length valuesare continuously recorded for three minutes.

In step 714, if after three minutes, the cycle length is greater thanthe cycle length measured three minutes previously, and the activitylevel is less than a threshold value, then in step 716 the largest cyclelength for each of the three minutes is recorded as the second set ofheart rate recovery values. If these criteria in step 714 are not met,then in step 718 a second heart rate is not recorded, as the exercise iscoming too slowly to a stop.

Once the cessation of exercise has been determined, in step 716 both thefirst, peak exercise heart rate and the three heart rate recovery valuecycle lengths are converted to beats per minute and subtracted from eachother. The values obtained are the times of heart rate recovery.

A method 800 of determining the duration of patient activity and patientexercise is illustrated in FIG. 8. According to an embodiment, themethod 800 begins at step 802, in which a patient-specific activitythreshold is calculated based on historical heart-rate data andhistorical activity-level data, which is collected over a period oftime, and predetermined patient-specific offset parameters. Detailsrelated to the predetermination of patient-specific offset parametersand the determination of the patient-specific activity threshold areprovided below with reference to FIGS. 9 and 10, respectively.

Continuing with FIG. 8, in step 804, current heart-rate and currentactivity level measurements are used to calculate a current activitycorrelation value (CORR_(current)) using the following equation:

CORR_(current)=HRR×(ACT_(current)−ACT_(offset)),

where HRR (heart rate reserve) is obtained as described below withreference to FIG. 10, step 1008; ACT_(current) is the activitymeasurement output by an activity sensor; and ACT_(offset) is obtainedas described below with reference to FIG. 9, step 908.

In a preferred embodiment, the heart rate and activity levelmeasurements are taken periodically, for example, every “x” seconds or“n” heart beats where “x” and “n” are programmed and may be smallquantities, such as 5 seconds and 3 heart beats, where resolutionincreases accuracy of detection. Periodic sampling is preferred overcontinuous measurements in order to conserve memory. In step 806, thecurrent activity correlation value (CORR_(current)) is compared to theactivity threshold (ACT THR). If the current correlation value does notexceed the activity threshold, the process returns to the step 804,where another current activity correlation value is calculated using thenext periodic current heart-rate and activity level measurements, andthe comparison process repeats. If at any time a current activitycorrelation value exceeds the activity threshold a beginning-of-activitytime-stamp is created at step 808.

At step 810, the method continues to calculate current activitycorrelation values and compares each to the activity threshold. At step812, if an activity correlation value fails to exceed the threshold, anend-of-activity time-stamp is created. As explained further below, thestart and end time stamps provide a measure of the duration of activity.Numerous activity durations over a period of time may be used to reportactivity related quantities. For example, a total daily activityduration can, be created by summing all the periods of activity thatoccurred over the course of a day.

At step 814, if current correlation values continue to exceed theactivity threshold value for a predetermined amount of time, theactivity, is considered to have been sustained long enough to qualify asexercise. For example, assuming the predetermined amount of time is fiveminutes, 10 consecutive activity correlation values (calculated every 30seconds) exceeding the activity threshold value would qualify theactivit as exercise. The predetermined amount of time is programmable.As explained further below, if activity is identified as exercise, thepredetermined amount of time associated with step 814 may besubsequently included in an exercise duration calculation.

At step 816, once an exercise state has been identified, the currentcorrelation values continue to be monitored and if they fall below theactivity threshold value for a predetermined amount of time the exerciseperiod is considered to have ended. This predetermined amount of timewhich, is also programmable, serves to distinguish between a pause inexercise and a stop of exercise. For example, for a predetermined amountof time equal to one minute, at least six consecutive currentcorrelation (CORR_(current)) values (calculated every 10 seconds) wouldhave to fail to exceed the activity threshold to signify an end ofexercise. Less than six current correlation values failing to exceed theactivity threshold would be considered a pause in exercise. As explainedfurther below, if an end of exercise is identified, the predeterminedamount of time associated with step 816 may be subsequently included inan exercise duration calculation.

With reference to FIG. 9, a method 900 of determining thepatient-specific offset parameters (ACT_(offset) and HR_(baseline)) isillustrated. The data collection of offset parameters are typicallyinitiated upon implant of the device, and may be periodically updated toaccount for changes in the patient's condition and sensitivity of thedevice sensors. The method involves the collection and analysis ofpatient data over a period of time, referred to as the“offset-acquisition time period.”

According to an embodiment, the method 900 begins at step 902, in whichthe offset-acquisition time period is determined. This time period maybe programmed into the device by the physician and is long enough toallow for the collection and analysis of a quantity of patient datasufficient to avoid the potential for inaccurate parameter offsetcalculations due to possible noise and atypical patient activity. In oneconfiguration, the offset-acquisition time, period is 24 hours. In step904 the offset-acquisition time period is partitioned into sub-timeperiods, for example, 1 hour time periods in the case of a 24 houroffset-acquisition time period. Though the use of sub-time periods isnot necessary, as will be apparent from the continuing explanation ofthe method 900, sub-time periods conserve memory space.

At step 906, for each sub-time period, heart rate and activity, levelare periodically measured, for example every 10 seconds. The patient'sheart rate may be determined by any suitable method. Many variations onhow to determine heart rate are known to those of ordinary skill in theart, and any of these of reasonable accuracy may be used. Heart rate canbe determined by measurement of an R-R interval cycle length (or P-P),which is the inverse of heart rate. As used herein, the heart rate (inbeats per minute) can be seen as the inverse to cycle length, determinedby 60,000 divided by the cycle length (in milliseconds).

The activity level of the patient may also be determined by any suitablemethod. For example, the activity level may be determined by anaccelerometer, piezoelectric crystal, minute ventilation, or aderivative thereof, such as the sensor indicated rate. In oneembodiment, activity level is determined using physiologic sensor 108.In this embodiment, sensor 108 is an accelerometer, a piezoelectriccrystal or an impedance sensor.

Continuing with FIG. 9, step 906, for each sub-time period, in anexemplary type of activity data analysis, a histogram of activity leveldata over the sub-time period is created. At the end of the sub-timeperiod, the activity level value appearing most frequent within the 1hour histogram is saved as the “peak bin” and the rest of the activitydata saved for the sub-time period is deleted from memory, therebyallowing the memory to be used again for the next sub-time period. Inaddition, at the end of each sub-time period, in an exemplary type ofheart-rate data analysis, a minimum heart rate measured in the sub-timeperiod is stored as HR_(sub-time min). The HR_(sub-time min) is notnecessarily the lowest heart rate measured but instead may be a heartrate that is close to the lowest measured rate in order to account forpossible noise.

At step 908, at the end of the time period, the activity offsetparameter (ACT_(offset)) is determined to be the minimum of theplurality of activity-level “peak bins.” The heart rate offset(HR_(baseline)) is determined to be the minimum of theHR_(sub-time min)'measured during the period of time.

With reference to FIG. 10, a method 1000 of determining thepatient-specific activity threshold (ACT THR) of step 802 of the methodof FIG. 8 is illustrated. The activity threshold is typically determinedupon implant of the device, and may be periodically updated to accountfor changes in the patient's condition and sensitivity of the devicesensors. The method involves the collection and analysis of patient dataover a period of time, referred to as the “activity threshold timeperiod.”

According to an embodiment, the method 1000 begins at step 1002, inwhich the activity threshold time period is determined. This time periodmay be programmed into the device by the physician and is long enoughto, allow for the collection and analysis of a quantity of patient datasufficient to avoid the potential for inaccurate activity thresholdcalculations due to possible noise and atypical patient activity. In oneconfiguration, the activity threshold time period is 7 days. In step1004 the activity threshold time period is partitioned into firstsub-time periods, for example 1 day time periods, in the case of a 7 dayactivity threshold time period. In step 1006 the first sub-time periodsare partitioned into second sub-time periods, for example, 1 hour timeperiods in the case of 1 day first sub-time periods. As described abovewith reference to the determination of patient-specific offsetparameters, the use of sub-time periods is not necessary, but ispreferred in order to conserve memory space.

At step 1008, for each second sub-time time period, heart rate andactivity level are periodically measured, for example every 10 seconds.The heart rate measurements are used in turn, to calculate a pluralityof heart-rate reserve values using the following equation:

${HRR} = {\frac{{HR} - {HR}_{baseline}}{{{Age}\mspace{14mu} {Compensated}\mspace{14mu} {Maximum}\mspace{14mu} {HR}} - {HR}_{baseline}} \times 100}$

Heart-rate reserve is used in the method instead of heart rate in orderto utilize a heart rate measurement that is normalized across thepatient population. Such normalization accounts for the fact that thesame heart rate may correspond to different activity states fordifferent people. For example, a heart rate of 90 beats per minute maycorrespond to sitting for one person and walking for another person. Inthe equation indicated above, HR (heart rate) may be obtained by anysuitable method; HR_(baseline) may be obtained as described withreference to FIG. 9, and age compensated maximum heart rate can becalculated by the formula: (220-age).

While the above HRR equation is expressed in terms of HR parameters,actual implementation of the above HRR equation may involve the use ofheart rate interval (HRI) calculations. In terms of HRI, the equationfor HRR becomes:

${HRR} = {\frac{\frac{{HRI}_{\min}}{HRI} \times \left( {{HRI}_{baseline} - {HRI}} \right)}{{HRI}_{baseline} - {HRI}_{\min}} \times 100}$

In the equation indicated above, HRI (heart rate interval) may beobtained by any suitable method; HRI_(min) may be obtained using theequation: 60,000/(220-age) and HRI_(baseline) may be obtained byconverting the HR_(baseline) measurement described with reference to.FIG. 9 into a heart rate interval measurement.

In order to reduce rounding errors when processing the above equation,it is desirable to perform all multiplication functions first in orderto make the numerator larger than the denominator. Considering the rangeof HRI in milliseconds, a 2 byte by 2 byte multiplier may be required toperform the multiplication operations. In addition, there are severalmultiplication and division operations in the HRR equation, which mayimpact processor duty cycle.

In an alternate embodiment, processing efficiency may be enhanced byobtaining an approximated heart-rate reserve using the followingequation:

HRR_(approx)=HR-HR_(baseline)

which in terms of HRI translates to:

HRR_(approx)=(60,000/HRI)−(60,000/HRI_(baseline))

This calculation reduces the number of arithmetic operations and may becompleted using a 1 byte by 1 byte multiplier; thereby reducing theprocessor duty cycle.

Continuing with step 1008, the activity level measurements are used, inturn to calculate a plurality of activity correlation values using thefollowing equation:

CORR_(sub-time)=HRR×(ACT_(sub-time)−ACT_(offset))

where ACT_(sub-time) is the activity measurement provided by theactivity sensor during the sub-time period; and ACT_(offset) is obtainedas described above with reference to FIG. 9, step 908. Note thatHRR_(approx) may be used in place of HRR in the CORR_(sub-time)calculation.

The sub-time correlation values are analyzed using, for example, ahistogram analysis similar to that described above with respect to FIG.9, step 906, to identify the sub-time correlation value appearing mostfrequent within the second sub-time period. A second sub-time activitythreshold is set at X% above the most frequent correlation value, whereX is programmable and may be for example, 70, 80 or 90. Setting thesecond sub-time activity threshold as a percentage above the mostfrequent correlation value provides for the filtering of noise andallows for a deterministic approach towards a threshold that encompassesa level of activity.. An example of a 1 hour histogram is shown in FIG.11.

Continuing with FIG. 10, in step 1010 after identifying the plurality ofsecond sub-time period activity thresholds, e.g., twenty-four, 1 hourthresholds in the case of a 1 day first sub-time period, the median ofthe plurality of second sub-time period activity thresholds isidentified as the activity threshold for that first sub-time period.While other statistical measurements may be used, the median value ischosen for robustness of the threshold, which will not be changed byseveral hours of atypical activities. The process is repeated for eachfirst sub-time period to identify a plurality of first sub-time periodactivity thresholds. Thus in the case of a 7 day activity threshold timeperiod, seven, 1 day activity thresholds would be determined. In step1012, at the end of the total activity threshold time period, e.g., 7days, the activity threshold (ACT THR) is set equal to the average ofthe plurality of first sub-time period, e.g., 1 day, activitythresholds. While other statistical measures may be used, an average ischosen to account for weekday and weekend difference of activitypatterns.

With reference to FIG. 12, various activity and exercise statesidentified over a period of time in accordance with the method 800described above with reference to FIG. 8 is presented in a time line toaid in describing possible activity duration and exercise durationcalculations. At time t₁, CORR_(current) exceeds ACT THR and a start ofactivity (NS) is identified. For each of times t₂ through t₇, CORRexceeds ACT THR. The time t₁ through t₅ corresponds to the predetermined“exercise” time used to identify exercise; accordingly, a start ofexercise (E/S) is identified at time t₅.

At time t₈ CORR did not exceed ACT THR and an end of activity (A/E) isidentified. At time t₁₀ CORR again exceeds ACT THR and a start ofactivity (A/S) is identified. Because the time between t₈ (when CORRfirst failed to exceed ACT THR) and t₁₀ (when CORR again exceeded ACTTHR) is less that the predetermined time used to identify the end ofexercise (an “exercise hysteresis”), this period of time is consideredto be a pause in exercise and there is no end of exerciseidentification. At time t₁₅, CORR failed to exceed ACT THR and an end ofactivity (A/E) is identified. At each of times t₁₆ through t₁₉, CORRfails to exceed ACT THR. Because the time between t₁₅ and t₁₉corresponds to the predetermined time used to identify the end ofexercise, an end of exercise (E/E) is identified at time t₁₉.

From the start and end identifications included in this exemplary timeline, activity duration would be calculated as A+B where A is theduration of the first activity period and B is the duration of thesecond activity period. Exercise duration would be calculated as C+D−E,where C is the difference between the start of exercise (E/S) and theend of exercise (E/E), D is the predetermined time used to identify thestart of exercise and E is the predetermined time used to identify theend of exercise.

With reference to FIGS. 13A-13D, benefits of the invention are notedupon comparison of various graphs of measured quantities as a functionof time during Stairmaster and treadmill exercises. To ensure ameaningful comparison, the graphs are based on exercise activity of thesame patient, with similar exercise timing and intensities. The measuredquantities include a heart-rate based measurement, e.g., HR reserve, andan activity measurement obtained using a one-dimensional physiologicalsensor oriented to detect horizontal movement but not vertical movement.

As shown in FIG. 13A, because of the vertical movement associated withStairmaster exercise, there is a significant disparity between themagnitude of heart-rate based measurements 1200 and activity-sensorbased measurements 1202. Notice that using activity sensor data only,Stairmaster exercise may not be detected at all due to low levelactivity measurements 1204. From FIG. 13B it is noted that using acorrelation value (Corr) 1206, which corresponds to a combination of theheart rate measurements and activity measurements of FIG. 13A, allowsfor better detection of Stairmaster exercise despite its low-levelactivity measurements. It is also noted that the correlation value isnot influenced by a relatively high heart rate measurement. In otherwords, if there is no activity but only a high heart rate, thecorrelation value will not be influenced. This is evident in FIG. 13Bnear the 8 minute mark 1208 where the graph indicates zero exerciseconsistent with the zero activity indicated near the 8 minute mark 1210in FIG. 13A.

With reference to FIG. 13C, because of the horizontal movementassociated with treadmill exercise, the disparity between the magnitudeof heart-rate based measurements 1200 and activity-sensor basedmeasurements 1202 is less pronounced than in the Stairmaster graph inFIG. 13A. The correlation value measurements 1206 plotted in FIG. 13Dcorrespond well with the heart-rate based measurements 1200 andactivity-sensor based measurements 1202 of FIG. 13C. From the foregoinggraphs it is noted that using the correlation value (Corr) allows forbetter detection of varying forms of exercise, regardless of thetendency of the exercise to involve horizontal or vertical movement.

As previously described, automatic determination of the activitythreshold may involve a scaling factor X %, which may be for example,80% above a correlation value arrived at using a histogram of historicalpatient data. For some people, however, this predetermined or defaultscaling factor may result in an over-sensitive activity threshold thatcauses the algorithm to consider a patient's daily activity such asoffice work, reading and talking as significant activity, or conversely,an under-sensitive threshold value that causes the algorithm to excludesignificant activity, such as walking, from its exercise diagnosticroutine. Therefore, verification, recalibration or optimization of theactivity threshold may be desirable.

One process for optimizing the activity threshold is based on acomparison of activity duration measurements provided by the patientdevice using the existing activity threshold and corresponding activityduration measurements provided by means external to the patient.Respective duration measurements for activity expected not to beconsidered significant activity are obtained from the patient device andthe external means. Alternatively, or in addition, respective durationmeasurements for low-level activity, which although low, is stillexpected to be considered significant activity, may be obtained from thepatient device and the external means.

Depending on the correspondence between the one or more patient-devicemeasurements and the one or more external-means measurements, anadjustment of the activity threshold may be warranted For example, fornon-significant activity expected to be undetected by the patientdevice, a patient-device measurement greater than zero may be anindication of an over-sensitive activity threshold, i.e., a thresholdthat is set too low. Accordingly, an increase in the activity thresholdmay be warranted. The magnitude of the increase,may be based on thedifference between the patient-device measurement and external meansmeasurement, with the magnitude of the increase, increasing as thedifference between the measurements decrease.

Below is a table of hypothetical patient-device measurements fornon-significant patient activity, e.g., desk work, talking, etc. thatoccurred for a period of 10 minutes. In order to ensure accurateresults, the non-significant activity is continuous for the entireperiod. In other words, the activity of the patient does not deviateinto either low-level significant activity, such as light walking, or noactivity at all. The table also includes a measurement of the percentagedifference between the patient-device measurement and the known durationof significant activity, and corresponding suggested activity thresholdadjustments. The known duration of significant activity is provided byan external measurement device, and is 0 minutes, i.e., 10 minutes ofknown non-significant patient activity corresponds to 0 minutes ofsignificant activity. For ease in presentation, duplicate measurementsare included, e.g. 0.5 min, 3.0 min and 6.0 min, across table rows. Inpractice however, a measurement would correspond to only one row. Thus,for example, the 0.5 minute upper limit in the first row may be “lessthan or equal to” 0.5 minute, while the 0.5 minute lower limit in thesecond row may be “greater than” 0.5 minute.

External-device Patient-device Suggested measurement of measurement ofthreshold significant activity significant activity % diff change 0 min0-.5 min.  ~0  0% 0 min .5-3.0 min. ≦30  +5% 0 min 3.0-6.0 min 30-60+10% 0 min 6.0-10 min  60-100 +15%

For low-level activity expected to be detected by the patient device, apatient-device measurement less than the known duration of the activitymay be an indication of under detection by an under-sensitive activitythreshold, i.e., a threshold that is set too high. Accordingly, adecrease in the activity threshold may be warranted. The magnitude ofthe decrease may be based on the difference between the patient-devicemeasurement and external means measurement, with the magnitude of thedecrease, increasing as the difference between the measurementsincrease.

Below is a table of hypothetical patient-device measurements forlow-level significant patient activity, e.g., light walking, thatoccurred for a period of 10 minutes. In order to ensure accurateresults, the low-level significant activity is continuous for the entireperiod. In other words, the activity of the patient does not deviateinto either non-significant activity or mid to high-level significantactivity. The table also includes a measurement of the percentagedifference between the patient-device measurement and the known durationof low-level significant activity, and corresponding suggested activitythreshold adjustments. The known duration of low-level significantactivity is provided by an external measurement device, and is 10minutes. As with the previous table, for ease in presentation, duplicatemeasurements are included, e.g. 9.0 min, 7.5 min and 2.5 min, acrosstable rows.

External-device Patient-device Suggested measurement of measurement ofthreshold significant activity significant activity % diff change 10 min9.0-10 min. <10  0% 10 min 7.5-9.0 min 10-25  −5% 10 min 2.5-7.5 min25-75 −10% 10 min 0-2.5 min  75-100 −15%

With reference to FIG. 14, an exemplary system 1300 for optimizing theactivity threshold includes a patient-associated medical device, such asan ICD 1302 and an external processor or programmer 13041 The externalprocessor 1304 includes an input for receiving data from the ICD 1302.Such data may be interrogated from the ICD using a telemetry wand or byRE telemetry means. The external processor 1304 also includes an inputfor receiving data other than from the ICD. Such data may be providedthrough a user interface 1306, e.g., a keyboard, and may include datarelated to external-means measurements. For example, an activity sessionof a patient may be monitored by a physician using a timer and theduration of the activity may be input to the processor 1304 through akeyboard. The user interface may also include a perception devicecapable of providing a perceptible indication, e.g., visual, sound, ofany suggested adjustments to the activity threshold.

A prediction model is programmed in the processor 1304. This modelprocesses measurements from the patient-device 1302 and measurementsfrom external means related to patient activity in a manner describedabove and provides adjustments to the activity threshold. Theseadjustments may be in the form of suggested increases or decrease inactivity threshold values provided to the physician on a display of theuser input device 306, in which case the physician ultimately decideswhether the suggested change will be implemented. Alternatively, theseadjustments may be telemetrically communicated to the patient device1302, in which case the changes may be automatically implemented by thepatient device.

With reference to FIG. 15, a method 1400 of ensuring optimization of theactivity threshold is illustrated. In block 1402 communication betweenan ICD and, external processor is established and the ICD is,interrogated to confirm the existence of a chronic activity threshold. Achronic activity threshold may be the initial activity thresholddetermined by the ICD after implant (as described above with referenceto FIG. 10) or a threshold previously obtained using the predicationmodel.

If a chronic activity threshold exists the process proceeds to block1404 where non-significant patient activity is monitored. For example,the patient may initiate and continue non-significant activity, e.g.,desk work, talking, for a specified period of time The actual durationof the non-significant activity, as provided by external means, is inputto the programmer. In block 1406, low-level, significant patientactivity is monitored. For example, the patient may initiate andcontinue low-level, significant activity, e.g., light walking, for aspecified period of time. The actual duration of the low-level,significant activity is input to the programmer. It is understood thatthe order in which the activity types are initiated is arbitrary, inthat low-level, significant activity may be initiated prior tonon-significant activity.

At block 1408, activity duration measurements for both thenon-significant and the low-level, significant activity are obtainedfrom the ICD. At block 1410, the predication model compares thenon-significant activity duration measurement from the ICD with theactual measurement from the external means and if necessary, provides asuggested scaling factor for the threshold based on the correspondencebetween the measurements. At block 1412, the prediction model comparesthe significant activity duration measurement from the ICD with theactual measurement from the external means and, if necessary, provides asuggested scaling factor for the activity threshold based on thecorrespondence between the measurements.

Exemplary scaling factors for increasing and decreasing the activitythreshold are provided in the previously described tables. Numerousother scaling schemes may be within the purview of those of ordinaryskill in the art. Accordingly, the invention is in no way limited to thescaling schemes presented in the tables.

With reference to FIG. 16A, heart rate reserve (HRR) measurements(indicated by dots) and activity sensor measurements (indicated bylines) are plotted as a function of time for a period of non-significantactivity, e.g., desk work and a period of low-level significantactivity, e,g., light walk, with a pause included between the twoperiods. In FIG. 16B, correlation values (CORR) derived from the HRR andactivity data of FIG. 16A are plotted as a function of time. Differentactivity threshold values (horizontal lines), including one consideredtoo high (resulting in undesirable non-detection of low-levelsignificant activity), one considered too low (resulting in undesirabledetection of non-significant activity) and one considered good(resulting in detection of low-level significant activity andnon-detection of non-significant activity).

It will be appreciated by those skilled in the art that the abovemethods 200, 400, 500, 600, 700, 800 and 1400 can be used within thehardware, software, and/or firmware of a pacing system, such as the ICDdescribed earlier with reference to FIGS. 1A and 1B, for example.

Example embodiments of the methods, systems, and components of thepresent invention have been described herein. As noted elsewhere, theseexample embodiments have been described for illustrative purposes only,and are not limiting. Other embodiments are possible within the scope ofthe invention. Such embodiments will be apparent to persons skilled inthe relevant art(s) based on the teachings contained herein. Forexample, while the prediction model has been described as residingwithin and being executed by an external processor, the model mayinstead reside in memory within a patient device. In this case, themodel could be initiated externally and executed using patient-deviceduration measurements stored within the device and external-meansduration measurements transmitted to the patient device. Also, while thedescription of the prediction model has focused on implantable patientdevices, the invention may find application in external patient devices.

Thus, the breadth and scope of the present invention should not belimited by any of the above-described exemplary embodiments, but shouldbe defined only in accordance with the following claims and theirequivalents.

1. For a patient-associated medical device operative to detect patientactivity based on a programmable activity threshold and to determine theduration of detected activity, a method of optimizing the activitythreshold comprising: obtaining first and second duration measurementsfor at least one of a first activity session and second activitysession, the first duration measurement based on the activity threshold,the second duration measurement based on actual start and stop of theactivity session; and suggesting an adjustment of the activity thresholdbased on at least one of correspondence between the first durationmeasurement and the second duration measurement of the first activitysession, and correspondence between the first duration measurement andthe second duration measurement of the second activity session.
 2. Themethod of claim 1 wherein the first activity corresponds to activityexpected to be undetected and suggesting an adjustment comprises, whenfirst activity is detected, suggesting an increase in the activitythreshold.
 3. The method of claim 2 wherein the suggested increase inthe activity threshold is based on the difference between the firstduration measurement and the second duration measurement of the firstactivity session.
 4. The method of claim 3 wherein the magnitude of thesuggested increase, increases as the difference between the firstduration measurement and the second duration measurement decreases. 5.The method of claim 1 wherein the second activity corresponds toactivity expected to be detected and suggesting an adjustment comprises,when second activity is under detected, suggesting a decrease in theactivity threshold.
 6. The method of claim 5 wherein the suggesteddecrease in activity threshold is based on the difference between thefirst duration measurement and the second duration measurement of thesecond activity session.
 7. The method of claim 6 wherein the magnitudeof the suggested decrease increases as the difference between the firstmeasurement and the second measurement increases.
 8. The method of claim1 wherein suggesting an adjustment of the activity threshold comprisesproviding a perceptible indication of the adjustment.
 9. The method ofclaim 1 wherein suggesting an adjustment of the activity thresholdcomprises automatically reprogramming the activity threshold.
 10. Asystem comprising: a patient-associated medical device operative todetect patient activity based on a programmable activity threshold, todetermine the duration of detected activity, and to output dataindicative of the duration; and a processor operative to: receive firstduration data from the medical device indicative of first durationmeasurements for each of a first activity session and a second activitysession; receive second duration data indicative of second durationmeasurements for each of the first activity session and the secondactivity session, the second duration measurements based on actual startand stop of the activity sessions; and suggest an adjustment of theactivity threshold based on at least one of correspondence between thefirst duration measurement and the second duration measurement of thefirst activity session, and correspondence between the first durationmeasurement and the second duration measurement of the second activitysession.
 11. The system of claim 10 wherein the patient-associatedmedical device is an implantable device.
 12. The system of claim 10wherein the processor is an implantable device.
 13. The system of claim10 further comprising a user interface operative to provide the secondduration data.
 14. The system of claim 13 wherein the user interface isfurther operative to provide a perceptible indication of the suggestedadjustment.
 15. The system of claim 10 wherein the first activitycorresponds to activity expected to be undetected and the processor isoperative to suggest an increase in the activity threshold when firstactivity is detected.
 16. The system of claim 10 wherein the secondactivity corresponds to activity expected to be detected and theprocessor is operative to suggest a decrease in the activity thresholdwhen second activity is under detected.
 17. The system of claim 10wherein the processor is further operative to automatically adjust theactivity threshold.
 18. A system associated with a patient-associatedmedical device operative to detect patient activity based on aprogrammable activity threshold and to determine the duration ofdetected activity, said system comprising: means for obtaining first andsecond duration measurements for at least one of a first activitysession and second activity session, the first duration measurementbased on the activity threshold, the second duration measurement basedon actual start and stop of the activity session; and means forsuggesting an adjustment of the activity threshold based on at least oneof correspondence between the first duration measurement and the secondduration measurement of the first activity session, and correspondencebetween the first duration measurement and the second durationmeasurement of the second activity session.
 19. The system of claim 18wherein means for suggesting comprises means for automaticallyreprogramming the activity threshold to correspond with the adjustment.20. The system of claim 18 wherein means for suggesting comprises meansfor providing a perceptible indication of the adjustment.